ŽUPERL, Uroš ;ČUŠ, Franci . Machining Process Optimization By Colony Based Cooperative Search Technique. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 54, n.11, p. 751-758, november 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/machining-process-optimization-by-colony-based-cooperative-search-technique/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/.
Župerl, U., & Čuš, F. (2008). Machining Process Optimization By Colony Based Cooperative Search Technique. Strojniški vestnik - Journal of Mechanical Engineering, 54(11), 751-758. doi:http://dx.doi.org/
@article{.,
author = {Uroš Župerl and Franci Čuš},
title = {Machining Process Optimization By Colony Based Cooperative Search Technique},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {11},
year = {2008},
keywords = {cutting parameters; machining; turning; optimization; },
abstract = {Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers.},
issn = {0039-2480}, pages = {751-758}, doi = {},
url = {https://www.sv-jme.eu/sl/article/machining-process-optimization-by-colony-based-cooperative-search-technique/}
}
Župerl, U.,Čuš, F. 2008 November 54. Machining Process Optimization By Colony Based Cooperative Search Technique. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 54:11
%A Župerl, Uroš %A Čuš, Franci %D 2008 %T Machining Process Optimization By Colony Based Cooperative Search Technique %B 2008 %9 cutting parameters; machining; turning; optimization; %! Machining Process Optimization By Colony Based Cooperative Search Technique %K cutting parameters; machining; turning; optimization; %X Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers. %U https://www.sv-jme.eu/sl/article/machining-process-optimization-by-colony-based-cooperative-search-technique/ %0 Journal Article %R %& 751 %P 8 %J Strojniški vestnik - Journal of Mechanical Engineering %V 54 %N 11 %@ 0039-2480 %8 2017-11-03 %7 2017-11-03
Župerl, Uroš, & Franci Čuš. "Machining Process Optimization By Colony Based Cooperative Search Technique." Strojniški vestnik - Journal of Mechanical Engineering [Online], 54.11 (2008): 751-758. Web. 19 Nov. 2024
TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci PY - 2008 TI - Machining Process Optimization By Colony Based Cooperative Search Technique JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - cutting parameters; machining; turning; optimization; N2 - Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers. UR - https://www.sv-jme.eu/sl/article/machining-process-optimization-by-colony-based-cooperative-search-technique/
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author = {Župerl, U., Čuš, F.},
title = {Machining Process Optimization By Colony Based Cooperative Search Technique},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {11},
year = {2008},
doi = {},
url = {https://www.sv-jme.eu/sl/article/machining-process-optimization-by-colony-based-cooperative-search-technique/}
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TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci PY - 2017/11/03 TI - Machining Process Optimization By Colony Based Cooperative Search Technique JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 54, No 11 (2008): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - cutting parameters, machining, turning, optimization, N2 - Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers. UR - https://www.sv-jme.eu/sl/article/machining-process-optimization-by-colony-based-cooperative-search-technique/
Župerl, Uroš, AND Čuš, Franci. "Machining Process Optimization By Colony Based Cooperative Search Technique" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 54 Number 11 (03 November 2017)
Strojniški vestnik - Journal of Mechanical Engineering 54(2008)11, 751-758
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers.